Quants in 2025 and beyond
Quants are financial professionals with backgrounds in technology, econometrics or mathematics, physics. They can take up different roles within the quantitative finance industry, including trading, research, risk management, product development, business development, and content creation. In this guide, we will focus only on the first three types of these quant roles.
Our understanding of the term “quants” has undergone significant changes over the past several decades. From what began as a restrictive domain inviting only PhDs and researchers from physics, mathematics & computational backgrounds, is now open to largely everyone who has a love for numbers and technology.
What has led to this change in the industry?
- Rise of algorithmic and high-frequency trading which expanded the trading domain significantly
- Rise of the availability of price data and alternative data allowing participants to analyse and trade
- Development of new languages such as Python, R, which are open-sourced
- Ease of online collaboration which enabled sharing of code libraries, ready-made tools and community support to newbies
- Rise of cloud computing infrastructure such as AWS which allows availability of computational power to a broader spectrum of people instead of needing to purchase expensive servers
In the infographic below, we have captured how the quantitative finance industry has transformed in the past 50 years.
History of Quant Finance Career
Typically Quants specialize and fall under the following categories:
- Quant Trader (Front office trader)
- Quant Analyst (Back office analyst)
- Quant Developer (Back office developer)
- Quantitative Risk Manager (Risk Analyst)
- Portfolio Manager/ Quant PM /Hedge Fund manager
- Quantitative Execution Trader/Researcher
Together, they build teams at trading firms which are responsible for trading trillions in the financial markets. A simple way to understand how Quants work together is by looking at the team structure of a high-frequency trading firm.
Organizational structure of ~100 team-sized Algorithmic Trading business
The role of a Quantitative Researcher (purple circle above) is the most versatile, as it spans across multiple teams, focusing on research and analysis. In contrast, Quant Trader roles are usually limited to the front office or trading desks. Quant Developers can work directly with traders and researchers on day-to-day trading operations or be part of the tech team, building and managing applications used across the company. Portfolio manager is responsible for optimizing the strategies, budget allocation, risk management. In this diagram, the head of strategies is playing the role of portfolio manager. The head of technology would primarily be responsible for achieving low latency solutions for the trading teams. In a way, we can call him the Execution Researcher. Depending on the team sizes, a few of these roles are combined to be handled by the same person. A 2-3 people team size setups usually consist of a developer, a trader and a quant researcher, with other tasks/responsibilities handled between them.
Quants in Banks Vs Trading Firms
To understand what skills, qualifications and experience are needed to get hired as a quant, it is best to understand what the need of the hiring firm is. Think as a recruiter. Once you understand how different firms operate, what their business models are, you can prepare yourself accordingly.
When you apply or consider a job in a trading or investment team, ask yourself these two very basic questions about the firm:
Do they primarily trade in exchanges or over-the-counter?
- Trading using structured products
- Trading using unstructured products
Do they invest/manage their own money or client’s money?
- Firms or divisions which trade to invest their own money
- Firms or divisions which trade on behalf of a client
Let us understand this with an example. Compare business models and financial markets participation in hedge funds vs proprietary trading firms.
While hedge funds manage money on behalf of external investors, proprietary (prop) trading firms invest their own money. This leads to differences in the business model. Prop trading firms profit from their trading and traders usually have profit sharing as part of their compensation. Whereas hedge funds earn fees based on AUM (assets under management) & performance fees. The risks appetite for the two business types differ greatly. Prop trading desks take higher risks since they trade on their own money. Hedge funds have to report risks and manage risk conservatively. The trading styles also vary with hedge funds usually catering to longer term strategies such as statistical arbitrage, quantitative equity investing, and event-driven strategies.
See the list of top HFT & Proprietary Trading Firms and Quant Investing firms.
Which one should you go for?
That depends on your skills and goals. Are you looking for an exciting profit sharing opportunity with direct impact in trading or are you looking for a more stable long term career? Do you want to work in a high energy, high risk environment or do you want to work in a more controlled and client facing environment? The first one is inclined to prop desks, HFT desks, Algo trading setups and the latter one is inclined towards hedge funds and quantitative investment firms.
Certifications for Quant Graduate Roles Vs Trading Roles
The demand for “quants” has been growing with the adoption of technology and computational methods in the financial markets industry. The number of academic degrees and certifications in the domain have increased keeping up with the increase in demand of the job markets. A significant number of the computer science graduates and engineers now move to finance post graduation. There is a difference between typical quant graduate roles and roles needed in firms/teams which directly trade in exchanges.
Degrees or certifications catering to the domain of quant finance prepare professionals for a wide range of activities beyond trading, such as risk modeling, financial engineering, investment strategy design, and building tools for pricing financial instruments.
Degrees or certifications catering specifically to algorithmic trading focus on practical hands-on curriculum which relies on using quantitative models to design, backtest, and execute trading strategies. Quant trading leverages data analysis, statistical arbitrage, algorithmic execution, and optimization techniques to identify and capitalize on market inefficiencies
Continue to read all about the differences between Quant Trading & Quant Finance careers. Irrespective of this niche difference, a quant role has evolved from a narrow focus on mathematical modeling to a multidisciplinary profession at the intersection of finance, technology, and data science. Today’s quants are expected to be versatile, blending coding skills with financial expertise and an understanding of emerging technologies. As financial markets evolve, the demand for adaptable, tech-savvy quants will only increase.
Take a 10-minute algo trading quiz to evaluate your skills under these three domains.
Why are multidisciplinary skills necessary for Quants
One of the key skills historically overlooked in the hiring process for a quant role is soft skills, or one’s ability to communicate clearly with people at different roles. This is an essential skill that can avoid losses at the trading desk, risk failures, and frustration at every step of a trading strategy execution.
To understand the need for this essential skill, let us first understand how different roles depend on each other. To simplify the processes, we can break down the core activities of running a trading strategy into four main areas.
| Stage | Key Roles | Key Activities |
|---|---|---|
| 1. Pre-Trade Activities | Quantitative Researcher | Model development, coding, backtesting, and optimization. |
| 2. Trading Using the Model | Quantitative Trader | Placing quotes, analyzing live trading, and managing market reactions. |
| 3. Risk Management | Risk Analyst | Risk measurement, exposure monitoring, hedging, and compliance. |
| 4. Quant Development | Quant Developer | Implementing models, optimizing infrastructure, and maintaining trading systems. |
Imagine a trading desk incurring a loss due to a coding error that failed to trigger a stop loss at the right time. Such mistakes often stem not from individual faults but from inefficiencies in team collaboration, typically caused by knowledge gaps.
For example, a developer with expertise in systems and infrastructure but limited understanding of financial markets and strategies may struggle to interpret strategy coding specifications for different market scenarios. Similarly, a researcher, analyst, or trader who defines strategy requirements without understanding programming structures, libraries, or model complexities may overlook critical scenarios.
3 Core Areas of expertise: Mathematics, Coding & Financial markets
At QuantInsti, we aspire to shape the quant finance space into an inclusive and empowering field. Our programs are designed to support aspiring quants from diverse backgrounds, strengths, and interests, inspiring them to confidently pursue a career in quant finance. We focus on strengthening your knowledge of three disciplines that are at the core of everything: mathematics, coding, & financial markets.
Self-Study Curriculum for Quants who want to join trading firms:
- Foundations and Financial Markets: Start by understanding the basics of quantitative finance, mathematics, stochastic calculus, statistics, macroeconomics, trading psychology, financial markets. This foundational knowledge helps you grasp how different market factors influence trading decisions. Find the detailed list of all blogs, books, courses we recommend to get you started for free on QuantInsti’s Github.
- Technical Skills Development: Gain proficiency in essential tools including Python, C++, R or Matlab for financial modeling, quantitative analysis, and algorithmic trading. Complement this with knowledge of database management for efficient data storage and retrieval. You can start with this free course on Python for Trading, perfect for beginners.
- Advanced Trading Strategies: Consider studying momentum, mean reversion, event-driven, and statistical arbitrage approaches. Focus on diverse instruments, including ETFs, options, futures, forex, bonds, high-frequency trading, and cryptocurrencies. Utilize quantitative models, technical indicators, time series analysis, and machine learning techniques. Leverage resources such as books by experts like Ernest Chan and Marcos López de Prado, Quantra courses, and insights from QuantInsti.If you are a serious learner, consider the trading strategies bundle at Quantra, which is also available at subscription prices.
- Machine Learning & AI: Learn how to collect, clean, and analyze financial data using data science techniques, including machine learning. Understanding data sources and advanced analytical methods such as LSTM networks, decision trees, neural networks and developing predictive trading models. Get a complete overview of using machine learning in trading in this free course on Quantra.
- Risk & Portfolio Management: Key topics include quantitative and active portfolio management, multi-strategy portfolios, machine learning applications, risk-adjusted returns, and optimization techniques like Monte Carlo simulation. If you are looking for a specialization in quantitative risk management, consider this learning track on Quantra.
- Backtesting, Execution, and Career Preparation: Practice backtesting trading strategies using Python, or through platform-based solutions. Transition from backtesting to live trading by setting up a trading desk and developing execution strategies. Finally, prepare for a career in quantitative finance by building a portfolio of projects, enhancing your resume, and practicing for interviews.
If you are looking to play a specific role in a firm, you can choose to read role-specific learning recommendations on the following positions
QuantInsti’s Executive Programme in Algorithmic Trading (EPAT)
EPAT is recommended for serious learners for those who are ready to commit to this field. A specialization in algorithmic trading, it is often pursued by financial market participants who wish to embrace technology and AI in their work. It is a fully online course with 120+ hours of live lectures, a faculty pool of 20+ practitioners and experts from all over the world, with focus on Python and practical implementation.. With dedicated mentorship, the programme enables participants of various backgrounds to join this exciting field.
Curriculum covered in EPAT is comprehensive and cutting edge, with lifetime access to improvements and additions to the curriculum.
How does QuantInsti help in career development?
- Practical Innovation: The curriculum continuously evolves, integrating emerging financial technologies, machine learning, and crypto applications.
- Transparent & Credible Learning: A structured and practical approach that ensures real-world relevance and employer trust.
- Continuous Learning Mindset: A focus on upskilling and adaptability to stay ahead in dynamic financial markets.
- Career-Focused Approach: Every aspect of EPAT is built to enhance job readiness and professional growth.
Sample set of quant job opportunities available to EPAT alumni.
